The problem of insufficient datasets has long been a hot topic in the field of prognosis and health management of rotary machines. Generative adversarial network (GAN) and other data augmentation algorithms can solve the problem of insufficient samples. However, the premise of the above method is the signal collected at a constant speed rather than at large speed fluctuation. To deal with data augmentation under large speed fluctuation, this paper proposes an effective deep learning method, namely, domain adaptive efficient sub-pixel network (DAESPN). The core idea of DAESPN is to enhance the resolution of the original sample for data augmentation. The DAESPN framework is implemented as follows: after the data passes through the fully connected neural network, the multi-feature maps of the four channels are outputted. A group of high resolution (HR) features is obtained through the sub-pixel fully connected layer. In addition, maximum mean discrepancy (MMD) and mean square error (MSE) are used to construct the loss function of the model. Experimental results of gearbox and bearing datasets show that the DAESPN model has strong feasibility to carry out data augmentation for fault diagnosis of rotating machines under speed fluctuation condition. In addition, the feature learning process of DAESPN is visually displayed and analyzed.

Changes in operating conditions often cause the distribution of signal features to shift during the bearing fault diagnosis process, which will result in reduced diagnostic accuracy of the model. Therefore, this paper proposes a dual-channel parallel adversarial network (DPAN) based on vision transformer, which extracts features from acoustic and vibration signals through parallel networks and enhances feature robustness through adversarial training during the feature fusion process. In addition, the Wasserstein distance is used to reduce domain differences in the fused features, thereby enhancing the network’s generalization ability. Two sets of bearing fault diagnosis experiments were conducted to validate the effectiveness of the proposed method. The experimental results show that the proposed method achieves higher diagnostic accuracy compared to other methods. The diagnostic accuracy of the proposed method can exceed 98%.

Data augmentation has become a hot topic in the field of mechanical intelligent fault diagnosis. It can expand the limited training dataset by generating simulated samples, but there is still no effective method augmenting the resolution of low resolution sample. In this paper, a simple algorithm, namely, efficient subpixel convolutional neural network (ESPCN), is proposed to solve this deficiency. The ESPCN model performs the arrange operation on the raw low resolution data through the subpixel layer and outputs the result of four‐channel multifeature maps. Then, the sample resolution is increased to four times compared with the raw low resolution sample. Finally, the generated high resolution dataset is employed to train the stacked autoencoders (SAE) for fault classification, and the raw high resolution dataset is used for testing. Two fault diagnosis cases with different sample dimensions and rotating speeds are set up to simulate the low resolution situation, and the experimental results verify the feasibility of the proposed algorithm.